UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 5
May-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2105894


Registration ID:
310149

Page Number

g713-g717

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Title

Multiclass Image Classification using CNN and CLAHE Hybrid Model at Runtime

Abstract

As data production has expanded at higher pace, the widespread use of automation and surveillance cameras and the requirement for visual feedback on artificially smart gadgets throughout the world has grown significantly the amount of image information that is now created. To simplify the image related jobs, the necessity for efficient image processing has increased. The classification of the categorical image takes thousands of pictures to train the algorithm. The system also needs a long time to extract the characteristics and to classify them. Thus, deep convolutional neural networks were created for image identification tasks and in recent years have shown promising results. CLAHE is a type of adaptive histogram equalization where contrast amplification is limited so that the noise amplification problem is minimised. This paper offers a hybrid CLAHE-deep convolutional neural network architecture for multiclass image classification at runtime. In order to effectively train the model, the suggested architecture is examined using multiple datasets. The experiment employs the Contrast Limited Adaptive Histogram Equalization (CLAHE) architecture as a pre-processing step for picture enhancement, followed by the CNN architecture for image classification. In terms of accuracy obtained, the experimental results imply that the suggested hybrid design beats traditional techniques.

Key Words

Image Classification, CNN, Deep Learning, Image Processing, Deep Networks, Neural Networks, CLAHE, Contrast Limited Adaptive Histogram Equalization

Cite This Article

"Multiclass Image Classification using CNN and CLAHE Hybrid Model at Runtime", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.g713-g717, May-2021, Available :http://www.jetir.org/papers/JETIR2105894.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Multiclass Image Classification using CNN and CLAHE Hybrid Model at Runtime", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppg713-g717, May-2021, Available at : http://www.jetir.org/papers/JETIR2105894.pdf

Publication Details

Published Paper ID: JETIR2105894
Registration ID: 310149
Published In: Volume 8 | Issue 5 | Year May-2021
DOI (Digital Object Identifier):
Page No: g713-g717
Country: Pune, Maharashtra, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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